Adaptive group search optimization algorithm for multi-objective optimal power flow problem

被引:120
|
作者
Daryani, Narges [1 ]
Hagh, Mehrdad Tarafdar [1 ]
Teimourzadeh, Saeed [1 ]
机构
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz, Iran
关键词
Optimal power flow (OPF); Group search optimization (GSO) algorithm; N-1 contingency analysis; Fuzzy logic; Pareto strategy; Adaptive GSO (AGSO); PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION; ECONOMIC-DISPATCH; WEAK BUSES; SECURITY CONSTRAINTS; IDENTIFICATION; EMISSION; SYSTEM; COST;
D O I
10.1016/j.asoc.2015.10.057
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an adaptive group search optimization (AGSO) algorithm for solving optimal power flow (OPF) problem. In this study, different aspects of the OPF problem are considered to form the accurate multi-objective model. The system total operation cost, the total emission, and N-1 security index are first, second, and third ordered objectives, respectively. Additionally, to consider accurate model of the problem, transmission losses and different equality and inequality constrains, such as feasible operating ranges of generators (FOR) and power flow equations are taken into account. Moreover, this study presents adaptive form of conventional GSO to precise the convergence characteristic of GSO. The effectiveness and accuracy of the proposed method for solving the nonlinear and nonconvex problems is validated by carrying out simulation studies on sample benchmark test cases and 30-bus and 57-bus IEEE standard test systems. Based on the comprehensive simulation studies, the accuracy of the proposed method is validated. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:1012 / 1024
页数:13
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